Search

The majority of human cancers evolve over time through the stepwise accumulation of somatic mutations followed by clonal selection akin to Darwinian evolution. However, the in-depth mechanisms that govern clonal dynamics and selection remain elusive, particularly during the earliest stages of tissue transformation. Cell competition (CC), often referred to as 'survival of the fittest' at the cellular level, results in the elimination of less fit cells by their more fit neighbors supporting optimal organism health and function. Alternatively, CC may allow an uncontrolled expansion of super-fit cancer cells to outcompete their less fit neighbors thereby fueling tumorigenesis. Recent research discussed herein highlights the various non–cell-autonomous principles, including interclonal competition and cancer microenvironment competition supporting the ability of a tumor to progress from the initial stages to tissue colonization. In addition, we extend current insights from CC-mediated clonal interactions and selection in normal tissues to better comprehend those factors that contribute to cancer development.

Regularization is essential for avoiding over-fitting to training data in network optimization, leading to better generalization of the trained networks. The label noise provides a strong implicit regularization by replacing the target ground truth labels of training examples by uniform random labels. However, it can cause undesirable misleading gradients due to the large loss associated with incorrect labels. We propose a first-order optimization method (Label-Noised Trim-SGD) that uses the label noise with the example trimming in order to remove the outliers based on the loss. The proposed algorithm is simple yet enables us to impose a large label-noise and obtain a better regularization effect than the original methods. The quantitative analysis is performed by comparing the behavior of the label noise, the example trimming, and the proposed algorithm. We also present empirical results that demonstrate the effectiveness of our algorithm using the major benchmarks and the fundamental networks, where our method has successfully outperformed the state-of-the-art optimization methods.

Development of multicellular organisms is orchestrated by persistent cell–cell communication between neighboring partners. Direct interaction between different cell types can induce molecular signals that dictate lineage specification and cell fate decisions. Current single-cell RNA-seq technology cannot adequately analyze cell–cell contact-dependent gene expression, mainly due to the loss of spatial information. To overcome this obstacle and resolve cell–cell contact-specific gene expression during embryogenesis, we performed RNA sequencing of physically interacting cells (PIC-seq) and assessed them alongside similar single-cell transcriptomes derived from developing mouse embryos between embryonic day (E) 7.5 and E9.5. Analysis of the PIC-seq data identified gene expression signatures that were dependent on the presence of specific neighboring cell types. Our computational predictions, validated experimentally, demonstrated that neural progenitor (NP) cells upregulate Lhx5 and Nkx2-1 genes, when exclusively interacting with definitive endoderm (DE) cells. Moreover, there was a reciprocal impact on the transcriptome of DE cells, as they tend to upregulate Rax and Gsc when in contact with NP cells. Using individual cell transcriptome data, we formulated a means of computationally predicting the impact of one cell type on the transcriptome of its neighboring cell types. We have further developed a distinctive spatial-t-distributed stochastic neighboring embedding to display the pseudospatial distribution of cells in a 2-dimensional space. In summary, we describe an innovative approach to study contact-specific gene regulation during embryogenesis.

Gene regulatory network inference from single-cell RNA sequencing (scRNAseq) datasets has an incredible potential to discover new regulatory rules. However, current computational inference methods often suffer from excessive predictions as existing strategies fail to remove indirect or false predictions. Here, we report a new algorithm single-cell multivariate Transfer Entropy, ‘scmTE’, that generates interpretable regulatory networks with reduced indirect and false predictions. By utilizing multivariate transfer entropy, scmTE accounts for gene-to-gene interdependence when quantifying regulatory relationships. Benchmarking against other methods using synthetic data manifested that scmTE is the unique algorithm that did not produce a hair-ball structure (due to too many predictions) and recapitulated known ground-truth relationships with high accuracy.In silicoknockdown experiments shows that scmTE assigns higher scores for specific interactions important for differentiation processes. We apply scmTE to T-cell differentiation, myelopoiesis and pancreatic development and identified known and novel regulatory interactions. scmTE provides a robust approach to infer interpretable networks by effectively removing unwanted indirect relationships.

Pancreatic and duodenal homeobox 1 (PDX1) is crucial for pancreas organogenesis, yet the dynamic changes in PDX1 binding in human or mouse developing pancreas have not been examined. To address this knowledge gap, we performed PDX1 ChIP-seq and single-cell RNA-seq using fetal human pancreata. We integrated our datasets with published datasets and revealed the dynamics of PDX1 binding and potential cell lineage-specific PDX1-bound genes in the pancreas from fetal to adult stages. We identified a core set of developmentally conserved PDX1-bound genes that reveal the broad multifaceted role of PDX1 in pancreas development. Despite the well-known dramatic changes in PDX1 function and expression, we found that PDX1-bound genes are largely conserved from embryonic to adult stages. This points towards a dual role of PDX1 in regulating the expression of its targets at different ages, dependent on other functionally congruent or directly interacting partners. We also showed that PDX1 binding is largely conserved in mouse pancreas. Together, our study reveals PDX1 targets in the developing pancreas in vivo and provides an essential resource for future studies on pancreas development.

Motivation Trajectory inference (TI) for single cell RNA sequencing (scRNAseq) data is a powerful approach to interpret dynamic cellular processes such as cell cycle and development. Still, however, accurate inference of trajectory is challenging. Recent development of RNA velocity provides an approach to visualize cell state transition without relying on prior knowledge. Results To perform TI and group cells based on RNA velocity we developed VeTra. By applying cosine similarity and merging weakly connected components, VeTra identifies cell groups from the direction of cell transition. Besides, VeTra suggests key regulators from the inferred trajectory. VeTra is a useful tool for TI and subsequent analysis. Availability and implementation The Vetra is available at https://github.com/wgzgithub/VeTra. Supplementary information Supplementary data are available at Bioinformatics online.

The basement membrane (BM) is a special type of extracellular matrix and presents the major barrier cancer cells have to overcome multiple times to form metastases. Here we show that BM stiffness is a major determinant of metastases formation in several tissues and identify netrin-4 (Net4) as a key regulator of BM stiffness. Mechanistically, our biophysical and functional analyses in combination with mathematical simulations show that Net4 softens the mechanical properties of native BMs by opening laminin node complexes, decreasing cancer cell potential to transmigrate this barrier despite creating bigger pores. Our results therefore reveal that BM stiffness is dominant over pore size, and that the mechanical properties of 'normal' BMs determine metastases formation and patient survival independent of cancer-mediated alterations. Thus, identifying individual Net4 protein levels within native BMs in major metastatic organs may have the potential to define patient survival even before tumour formation. The ratio of Net4 to laminin molecules determines BM stiffness, such that the more Net4, the softer the BM, thereby decreasing cancer cell invasion activity. The manuscript has been formally accepted for publication by Nature Materials. The manuscript needs shortening of the body, wherefore, final acceptance will be given upon re-submission of the shortened manuscript body. ispartof: Nature Materials vol:20 issue:6 ispartof: location:England status: Published online

Most AML patients exhibit mutational activation of the PI3K/AKT signaling pathway, which promotes downstream effects including growth, survival, DNA repair, and resistance to chemotherapy. Herein we demonstrate that the inv(16)/KITD816Y AML mouse model exhibits constitutive activation of PI3K/AKT signaling, which was enhanced by chemotherapy-induced DNA damage through DNA-PK-dependent AKT phosphorylation. Strikingly, inhibitors of either PI3K or DNA-PK markedly reduced chemotherapy-induced AKT phosphorylation and signaling leading to increased DNA damage and apoptosis of inv(16)/KITD816Y AML cells in response to chemotherapy. Consistently, combinations of chemotherapy and PI3K or DNA-PK inhibitors synergistically inhibited growth and survival of clonogenic AML cells without substantially inhibiting normal clonogenic bone marrow cells. Moreover, treatment of inv(16)/KITD816Y AML mice with combinations of chemotherapy and PI3K or DNA-PK inhibitors significantly prolonged survival compared to untreated/single-treated mice. Mechanistically, our findings implicate that constitutive activation of PI3K/AKT signaling driven by mutant KIT, and potentially other mutational activators such as FLT3 and RAS, cooperates with chemotherapy-induced DNA-PK-dependent activation of AKT to promote survival, DNA repair, and chemotherapy resistance in AML. Hence, our study provides a rationale to select AML patients exhibiting constitutive PI3K/AKT activation for simultaneous treatment with chemotherapy and inhibitors of DNA-PK and PI3K to improve chemotherapy response and clinical outcome.

Cells have evolved communication methods to sense their microenvironments and send biological signals. In addition to the communication using ligands and receptors, cells use diverse channels including gap junctions to communicate with their immediate neighbors. Current approaches, however, cannot effectively capture the influence of various microenvironments. Here, we propose a novel approach that identifiescell neighbor-dependent geneexpression (CellNeighborEX). After categorizing cells based on their microenvironment from spatial transcriptomics (ST) data, CellNeighborEX identifies diverse gene sets associated with partnering cell types, providing further insight. To categorize cells along with their environment, CellNeighborEX uses direct cell location or the mixture of transcriptome from multiple cells depending on the ST technology. We show that cells express different gene sets depending on the neighboring cell types in various tissues including mouse embryos, brain, and liver cancer. These genes were associated with development (in embryos) or metastases (liver cancer). We further validate that gene expression can be induced by neighboring partners. The neighbor-dependent gene expression suggests new potential genes involved in cell-cell interactions beyond what ligand-receptor co-expression can discover.

Autophagy, a catabolic process to remove unnecessary or dysfunctional organelles, is triggered by various signals including nutrient starvation. Depending on the types of the nutrient deficiency, diverse sensing mechanisms and signaling pathways orchestrate for transcriptional and epigenetic regulation of autophagy. However, our knowledge about nutrient type-specific transcriptional regulation during autophagy is limited. To understand nutrient type-dependent transcriptional mechanisms during autophagy, we performed single cell RNA sequencing (scRNAseq) in the mouse embryonic fibroblasts (MEFs) with or without glucose starvation (GS) as well as amino acid starvation (AAS). Trajectory analysis using scRNAseq identified sequential induction of potential transcriptional regulators for each condition. Gene regulatory rules inferred using TENET newly identified CCAAT/enhancer binding protein γ (C/EBPγ) as a regulator of autophagy in AAS, but not GS, condition, and knockdown experiment confirmed the TENET result. Cell biological and biochemical studies validated that activating transcription factor 4 (ATF4) is responsible for conferring specificity to C/EBPγ for the activation of autophagy genes under AAS, but not under GS condition. Together, our data identified C/EBPγ as a previously unidentified key regulator under AAS-induced autophagy.

Large-scale multiparameter screening has become increasingly feasible and straightforward to perform thanks to developments in technologies such as high-content microscopy and high-throughput flow cytometry. The automated toolkits for analyzing similarities and differences between large numbers of tested conditions have not kept pace with these technological developments. Thus, effective analysis of multiparameter screening datasets becomes a bottleneck and a limiting factor in unbiased interpretation of results. Here we introduce compaRe, a toolkit for large-scale multiparameter data analysis, which integrates quality control, data bias correction, and data visualization methods with a mass-aware gridding algorithm-based similarity analysis providing a much faster and more robust analyses than existing methods. Using mass and flow cytometry data from acute myeloid leukemia and myelodysplastic syndrome patients, we show that compaRe can reveal interpatient heterogeneity and recognizable phenotypic profiles. By applying compaRe to high-throughput flow cytometry drug response data in AML models, we robustly identified multiple types of both deep and subtle phenotypic response patterns, highlighting how this analysis could be used for therapeutic discoveries. In conclusion, compaRe is a toolkit that uniquely allows for automated, rapid, and precise comparisons of large-scale multiparameter datasets, including high-throughput screens.Biology has seen huge advances in technology in recent years. This has led to state-of-the-art techniques which can test hundreds of conditions simultaneously, such as how cancer cells respond to different drugs. In addition to this, each of the tens of thousands of cells studied can be screened for multiple variables, such as certain proteins or genes. This generates massive datasets with large numbers of parameters, which researchers can use to find similarities and differences between the tested conditions. Analyzing these ‘high-throughput’ experiments, however, is no easy task, as the data is often contaminated with meaningless information, or ‘background noise’, as well as sources of bias, such as non-biological variations between experiments. As a result, most analysis methods can only probe one parameter at a time, or are unautomated and require manual interpretation of the data. Here, Chalabi Hajkarim et al. have developed a new toolkit that can analyze multiparameter datasets faster and more robustly than current methods. The kit, which was named ‘compaRe’, combines a range of computational tools that automatically ‘clean’ the data of background noise or bias: the different conditions are then compared and any similarities are visually displayed using a graphical interface that is easy to explore. Chalabi Hajkarim et al. used their new method to study data from patients with acute myeloid leukemia (AML) and myelodysplastic syndrome, two forms of cancer that disrupt the production of functional immune cells. The toolkit was able to identify subtle differences between the patients and categorize them into groups based on the proteins present on immune cells. Chalabi Hajkarim et al. also applied compaRe to high-throughput data on cells from patients and mouse models with AML that had been treated with large numbers of specific drugs. This revealed that different cell types in the samples responded to the treatments in distinct ways. These findings suggest that the toolkit created by Chalabi Hajkarim et al. can automatically, rapidly and precisely compare large multiparameter datasets collected using high-throughput screens. In the future, compaRe could be used to identify drugs that illicit a specific response, or to predict how newly developed treatments impact different cell types in the body.

SummaryDevelopment of multicellular organisms is orchestrated by persistent cell-cell communication between neighboring partners. Direct interaction between different cell types can induce molecular signals that dictate lineage specification and cell fate decisions. Current single cell RNAseq (scRNAseq) technology cannot adequately analyze cell-cell contact-dependent gene expression, mainly due to the loss of spatial information. To overcome this obstacle and resolve cell-cell contact-specific gene expression during embryogenesis, we performed RNA sequencing of physically interacting cells (PIC-seq) and assessed them alongside similar single cell transcriptomes derived from developing mouse embryos between embryonic day (E) 7.5 and E9.5. Analysis of the PIC-seq data identified novel gene expression signatures that were dependent on the presence of specific neighboring cell types. Our computational predictions, validated experimentally, demonstrated that neural progenitor (NP) cells overexpress Lhx5 and Nkx2-1 genes, when exclusively interacting with the definitive endoderm (DE) cell. Moreover, there was a reciprocal impact on the transcriptome of the DE cells, as they tend to overexpress Rax and Gsc genes when in contact with the NP cells. Using individual cell transcriptome data, we formulated a means of computationally predicting the impact of one cell type on the transcriptome of its neighboring cell types. We have further developed a distinctive spatial-tSNE to display the pseudo-spatial distribution of cells in a 2-dimensional space. In summary, we describe an innovative approach to study contact-specific gene regulation during embryogenesis with potential broader implication in other physiologically relevant processes.SignificancePhysical contact between neighboring cells is known to induce transcriptional changes in the interacting partners. Accurate measurement of these cell-cell contact based influences on the transcriptome is a very difficult experimental task. However, determining such transcriptional changes will highly enhance our understanding for the developmental processes. Current scRNAseq technology isolates the tissue into individual cells, making it hard to determine the potential transcriptomic changes due to its interacting partners. Here, we combined PIC-seq and computational algorithms to identify cell-type contact dependent transcriptional profiles focusing on endoderm development. We have computationally identified and experimentally validated specific gene expression patterns depending upon the presence of specific neighboring cell types. Our study suggests a new way to study cell-cell interactions for embryogenesis.

Risk stratification of COVID‐19 patients is essential for pandemic management. Changes in the cell fitness marker, hFwe‐Lose, can precede the host immune response to infection, potentially making such a biomarker an earlier triage tool. Here, we evaluate whether hFwe‐Lose gene expression can outperform conventional methods in predicting outcomes (e.g., death and hospitalization) in COVID‐19 patients. We performed a post‐mortem examination of infected lung tissue in deceased COVID‐19 patients to determine hFwe‐Lose’s biological role in acute lung injury. We then performed an observational study (n = 283) to evaluate whether hFwe‐Lose expression (in nasopharyngeal samples) could accurately predict hospitalization or death in COVID‐19 patients. In COVID‐19 patients with acute lung injury, hFwe‐Lose is highly expressed in the lower respiratory tract and is co‐localized to areas of cell death. In patients presenting in the early phase of COVID‐19 illness, hFwe‐Lose expression accurately predicts subsequent hospitalization or death with positive predictive values of 87.8–100% and a negative predictive value of 64.1–93.2%. hFwe‐Lose outperforms conventional inflammatory biomarkers and patient age and comorbidities, with an area under the receiver operating characteristic curve (AUROC) 0.93–0.97 in predicting hospitalization/death. Specifically, this is significantly higher than the prognostic value of combining biomarkers (serum ferritin, D‐dimer, C‐reactive protein, and neutrophil–lymphocyte ratio), patient age and comorbidities (AUROC of 0.67–0.92). The cell fitness marker, hFwe‐Lose, accurately predicts outcomes in COVID‐19 patients. This finding demonstrates how tissue fitness pathways dictate the response to infection and disease and their utility in managing the current COVID‐19 pandemic., A post‐mortem examination of COVID‐19 infected lung tissues and an observational study were performed in order to evaluate whether expression of cell fitness marker hFwe‐Lose in patient's nasopharyngeal swabs could predict hospitalization or death from COVID‐19.

Autism spectrum disorders (ASDs) are common neurodevelopmental disorders characterized by deficits in social interactions and communication, restricted interests, and repetitive behaviors. Despite extensive study, the molecular targets that control ASD development remain largely unclear. Here, we report that the dormancy of quiescent neural stem cells (qNSCs) is a therapeutic target for controlling the development of ASD phenotypes driven by Shank3 deficiency. Using single-cell RNA sequencing (scRNA-seq) and transposase accessible chromatin profiling (ATAC-seq), we find that abnormal epigenetic features including H3K4me3 accumulation due to up-regulation of Kmt2a levels lead to increased dormancy of qNSCs in the absence of Shank3. This result in decreased active neurogenesis in the Shank3 deficient mouse brain. Remarkably, pharmacological and molecular inhibition of qNSC dormancy restored adult neurogenesis and ameliorated the social deficits observed in Shank3-deficient mice. Moreover, we confirmed restored human qNSC activity rescues abnormal neurogenesis and autism-like phenotypes in SHANK3-targeted human NSCs. Taken together, our results offer a novel strategy to control qNSC activity as a potential therapeutic target for the development of autism.

Tumors are complex cellular and acellular environments within which cancer clones are under continuous selection pressures. Cancer cells are in a permanent mode of interaction and competition with each other as well as with the immediate microenvironment. In the course of these competitive interactions, cells share information regarding their general state of fitness, with less-fit cells being typically eliminated via apoptosis at the hands of those cells with greater cellular fitness. Competitive interactions involving exchange of cell fitness information have implications for tumor growth, metastasis, and therapy outcomes. Recent research has highlighted sophisticated pathways such as Flower, Hippo, Myc, and p53 signaling, which are employed by cancer cells and the surrounding microenvironment cells to achieve their evolutionary goals by means of cell competition mechanisms. In this review, we discuss these recent findings and explain their importance and role in evolution, growth, and treatment of cancer. We further consider potential physiological conditions, such as hypoxia and chemotherapy, that can function as selective pressures under which cell competition mechanisms may evolve differently or synergistically to confer oncogenic advantages to cancer.

Deep neural networks (DNNs) have achieved great success in the last decades. DNN is optimized using the stochastic gradient descent (SGD) with learning rate annealing that overtakes the adaptive methods in many tasks. However, there is no common choice regarding the scheduled-annealing for SGD. This paper aims to present empirical analysis of learning rate annealing based on the experimental results using the major data-sets on the image classification that is one of the key applications of the DNNs. Our experiment involves recent deep neural network models in combination with a variety of learning rate annealing methods. We also propose an annealing combining the sigmoid function with warmup that is shown to overtake both the adaptive methods and the other existing schedules in accuracy in most cases with DNNs.

Transitional hypoglycemia in normal newborns occurs in the first 3 days of life and has clinical features consistent with hyperinsulinism. We found a lower threshold for glucose-stimulated insulin secretion from freshly isolated embryonic day (E) 22 rat islets, which persisted into the first postnatal days. The threshold reached the adult level by postnatal day (P) 14. Culturing P14 islets also decreased the glucose threshold. Freshly isolated P1 rat islets had a lower threshold for insulin secretion in response to 2-aminobicyclo-(2, 2, 1)-heptane-2-carboxylic acid, a nonmetabolizable leucine analog, and diminished insulin release in response to tolbutamide, an inhibitor of β-cell KATP channels. These findings suggested that decreased KATP channel function could be responsible for the lower glucose threshold for insulin secretion. Single-cell transcriptomic analysis did not reveal a lower expression of KATP subunit genes in E22 compared with P14 β cells. The investigation of electrophysiological characteristics of dispersed β cells showed that early neonatal and cultured cells had fewer functional KATP channels per unit membrane area. Our findings suggest that decreased surface density of KATP channels may contribute to the observed differences in glucose threshold for insulin release.

Islet function is critical for normal glucose homeostasis. Unlike adult β cells, fetal and neonatal islets are more proliferative and have decreased insulin secretion in response to stimuli. However, the underlying mechanisms governing functional maturity of islets have not been completely elucidated. Pancreatic islets comprise different cell types. The microenvironment of islets and interactions between these cell types are critical for β-cell development and maturation. Thus, the study of intact islets is optimal to identify novel molecular mechanisms controlling islet functional development. Transcriptomes and genome-wide histone landscapes of H3K4me3, H3K27me3, and H3K27Ac from intact islets isolated from 2- and 10-week-old Sprague-Dawley rats were integrated to elucidate genes and pathways modulating islet development, as well as the contribution of epigenetic regulation. A total of 4489 differentially expressed genes were identified; 2289 and 2200 of them were up- and down-regulated in 10-week islets, respectively. Ingenuity Pathway Analysis revealed critical pathways regulating functional maturation of islets, including nutrient sensing, neuronal function, immune function, cell replication, and extracellular matrix. Furthermore, we identified significant changes in enrichment of H3K4me3, H3K27me3, and H3K27Ac marks, which correlated with expression changes of genes critical for islet function. These histone marks were enriched at critical transcription factor-binding motifs, such as Hoxa9, C/EBP-β, Gata1, Foxo1, E2f1, E2f3, and Mafb. In addition, our chromatin immunoprecipitation sequencing data revealed multiple potential bivalent genes whose poised states changed with maturation. Collectively, our current study identified critical novel pathways for mature islet function and suggested a role for histone modifications in regulating islet development and maturation.

Hypoxic insults in the perinatal period can lead to persistent hyperinsulinism and profound hypoglycemia in newborns. We studied the impact of the hypoxia-inducible factor 1A (HIF1A) pathway on postnatal β-cell function. Rat pups were treated daily between postnatal day (P)7 to P10 with adaptaquin (AQ), an inhibitor of prolyl hydroxylases, which stabilizes HIF1A. AQ-treated pups were hypoglycemic and had higher plasma insulin concentrations. Their islets had a decreased glucose threshold for insulin secretion, indicative of a delay in β-cell postnatal functional maturation. Histology analyses revealed that AQ-treated pups had increased pancreatic insulin-positive area but no changes in the number of islets or number of β-cells per islet, suggesting larger average β-cell size. AQ-treated rat pups had decreased expression of cell cycle genes and decreased numbers of proliferating β-cells. In conclusion, pharmacologic activation of the HIF1A pathway in the early postnatal period leads to hyperinsulinism, due to the persistence of a low glucose threshold for insulin secretion, and to decreased early postnatal β-cell proliferation, suggesting it can impact adult β-cell mass and diabetes risk.

Retinoids are essential endogenous compounds involved in regulation of critical biologic processes, including maintenance of metabolic homeostasis in the liver. Much of the knowledge of altered retinoid homeostasis in human disease states is derived from changes in indirect markers such as mRNA expression of retinoid-related genes and circulating concentrations of retinol or its binding protein RBP4. We hypothesized that in the human liver, concentrations of the active retinoid all-trans-retinoic acid (atRA) correlate with the concentrations of retinyl palmitate (RP), the storage form of atRA, retinol, the inactive vitamin A, and the mRNA expression of retinoid-related genes. On the basis of existing knowledge of altered vitamin A homeostasis in metabolic syndrome, we also predicted that in human livers with nonalcoholic fatty liver disease (NAFLD) retinoid concentrations would be decreased. Using novel liquid chromatography–tandem mass spectrometry methods, the hepatic vitamin A metabolome was quantified in normal human livers (n = 50) and 22 livers from donors with NAFLD. The hepatic concentrations of RP, atRA, 13-cisRA, and 4-oxo-atRA were significantly decreased in NAFLD samples in comparison with normal liver samples, whereas retinol levels remained unchanged. The concentrations of atRA were positively correlated with RP and 13-cisRA but not with retinol or the relative mRNA expression of LRAT, ALDH1A1, CYP26A1, RARα, and RARβ. An active metabolite of atRA, 4-oxo-atRA was, for the first time, detected in human tissues at comparable concentration with RA isomers, suggesting this retinoid may contribute to retinoid signaling in humans. SIGNIFICANCE STATEMENT: This study shows that in NAFLD liver vitamin A homeostasis is disrupted potentially contributing to disease progression. The results show that interpretation of retinoid homeostasis on the basis of indirect markers such as retinol concentrations or mRNA data is probably misleading when evaluating human disease processes, and analysis of the broader retinoid metabolome is needed to characterize disease effects on retinoid signaling.

OabjectiveTransitional hypoglycemia in normal newborns occurs in the first 3 days of life and has clinical features consistent with hyperinsulinism. We hypothesized that this transitional hyperinsulinism is due to the persistence of a fetal lower glucose threshold for insulin release from {beta}-cells into the first postnatal days. MethodsWe tested dynamic insulin secretion from freshly isolated rat islets between late gestation and adult age and from rat islets kept in culture for 1 or 2 days. We used single-cell transcriptomic and electrophysiology approaches to investigate the mechanism for insulin secretion at low glucose concentrations. ResultsWe found that a lower threshold for glucose-stimulated insulin secretion (GSIS) is present in embryonic day (E)22 islets and persists into the first postnatal days. The glucose threshold increases in the postnatal period and reaches the adult level by postnatal day (P)14. We also demonstrated that culturing P14 islets for 24-48 hrs can also decrease the glucose threshold. Insulin release in response to BCH, a non-metabolizable leucine analog activating glutamate dehydrogenase, had a similar lower threshold in P1 compared to P14 islets. This showed that the low threshold for GSIS is determined at a step downstream of the glycolytic pathway. P1 islets had lower insulin release in response to tolbutamide, an inhibitor of {beta}-cell KATP channels, compared to P14 islets, suggesting that decreased KATP channel expression and/or function could be responsible for the lower glucose threshold for insulin secretion. Singlecell transcriptomic analysis did not reveal differences in transcripts between E22 and P14 {beta}-cells supporting the lower glucose threshold. The investigation of electrophysiological characteristics of dispersed {beta}-cells showed that early neonatal cells and cultured islet cells had fewer functional KATP channels per unit membrane area. ConclusionThese findings suggest that decreased surface density of KATP channels may contribute to the observed differences in glucose threshold for insulin release.

SummaryPancreatic and duodenal homeobox 1 (PDX1)is crucial for pancreas organogenesis, yet the dynamic changes in PDX1 targets in mouse or human pancreas development have not been examined. We integrated the PDX1 cistrome with cell lineage-specific gene expression in both mouse and human developing pancreas. We identified a core set of developmentally and evolutionarily conserved PDX1 bound genes that reveal the broad multifaceted role of PDX1 in pancreas development. Despite the well-known, dramatic changes in PDX1 function and expression, we showed that PDX1 binding is largely stable from embryonic pancreas to adult islet. This may point towards a dual role of PDX1, activating or repressing the expression of its targets at different ages, dependent on other functionally-congruent or directly-interacting partners. Our work also suggests that PDX1 functions not only in initiating pancreas differentiation, but also as a potential keepsake of the progenitor program in the adult beta cells.

Multiparametric phenotypic screening of cells, for example assessing their responses to small molecules or knockdown/knockout of specific genes, is a powerful approach to understanding cellular systems and identifying potential new therapeutic strategies. However, automated tools for analyzing similarities and differences between a large number of tested conditions have not been readily available. Methods designed for clustering cells cannot identify differences between samples effectively. We introduce compaRe for ultra-fast and robust analysis of multiparametric high-throughput screening. Applying a mass-aware gridding algorithm using hypercubes, compaRe performs automatic and effective similarity comparison for hundreds to thousands of tests and provides information about the treatment effect. Particularly for screening data, compaRe is equipped with modules to remove various sources of bias.Benchmarking tests show that compaRe can circumvent batch effects and perform a similarity analysis substantially faster than conventional analysis tools. Applying compaRe to high-throughput flow cytometry screening data, we were able to distinguish subtle phenotypic drug responses in a human sample and a genetically engineered mouse model with acute myeloid leukemia (AML). compaRe revealed groups of drugs with similar responses even though their mechanisms are distinct from each other. In another screening, compaRe effectively circumvented batch effects and grouped samples from AML and myelodysplastic syndrome (MDS) patients using clinical flow cytometry data.

Autophagy is a catabolic process through which cytoplasmic components are degraded and recycled in response to various stresses including starvation. Recently, transcriptional and epigenetic regulations of autophagy have emerged as essential mechanisms for maintaining homeostasis. Here, we identify that coactivator-associated arginine methyltransferase 1 (CARM1) methylates Pontin chromatin-remodeling factor under glucose starvation, and methylated Pontin binds Forkhead Box O 3a (FOXO3a). Genome-wide analyses and biochemical studies reveal that methylated Pontin functions as a platform for recruiting Tip60 histone acetyltransferase with increased H4 acetylation and subsequent activation of autophagy genes regulated by FOXO3a. Surprisingly, CARM1-Pontin-FOXO3a signaling axis can work in the distal regions and activate autophagy genes through enhancer activation. Together, our findings provide a signaling axis of CARM1-Pontin-FOXO3a and further expand the role of CARM1 in nuclear regulation of autophagy., Epigenetic regulations of autophagy have emerged as mechanisms for maintaining cellular homeostasis. Here the authors reveal that the CARM1-Pontin-FOXO3a signaling axis can activate autophagy related genes through enhancer activation.

EBV latent infection in gastric carcinoma (GC) cells is characterized by distinct viral gene expression programs. CCCTC-binding factor (CTCF) is a chromatin structural factor that has been involved in coordinated chromatin interactions between multiple loci of Epstein-Barr virus (EBV) genes. Here, we investigate the role of CTCF in regulating EBV gene expression and chromosome conformation in model of EBV-associated gastric carcinoma (EBVaGC). Chromatin immunoprecipitation followed by sequencing (ChIP-seq) against CTCF revealed 16 CTCF binding sites (BS) in EBV genome of EBVaGC, SNU719 cells. Among the CTCF BSs, one site named as BARTp (BamHI A right transcript promoter) CTCF BS is located at upstream of 11.8-kb BART region (EBV genome: 139724-151554) and was not yet defined its biological functions in EBV life cycle. EBV BART encodes a complex miRNA cluster of highly spliced transcripts that is implicated in EBV cancer pathogenesis. This present study investigated the functional role of the CTCF binding site at BARTp (BARTp CTCF BS) in regulating EBV gene transcription and EBV three-dimensional (3D) genome structure as DNA loop maker. Circular chromatin confirmation capture (4C)-seq and chromatin confirmation capture (3C)-semi-quantitative(sq)PCR assays using SNU719 cells revealed that BARTp CTCF BS interacts with CTCF BSs of LMP1/2, Cp/OriP, and Qp in EBV genome. We generated mutations in BARTp CTCF BS (S13) in bacmids with (BART+) or without (BART−) the 11.8-kb BART transcript unit (B(+/−)). ChIP-qPCR assay demonstrated that CTCF binding was ablated from BARTp in EBV B(+/−) S13− genomes (mutant S13), elevated at several other sites such as LMP1, OriP, and Cp in EBV B(-) (BART−) S13− genome, and decreased at the same sites in EBV B(+) S13− genome. Infection assay showed that BARTp CTCF BS mutation reduced infectivity, while BART transcript deletion has no detectable effects. Gene expression tests showed that EBNA1 was highly downregulated in B(+/−) S13− EBVs related to B(+/−) S13+ EBVs (wild-type S13). LMP1 and BZLF1 were more downregulated in B(-) S13− EBV than B(+) S13− EBV. Taken together, these findings suggest that the CTCF binding and BART region contribute to EBV 3D genome structure via a cluster of DNA loops formed by BARTp CTCF BS (S13) and are important for coordinated viral gene expression and EBV infectivity.

Trajectory inference (TI) for single cell RNA sequencing (scRNAseq) data is a powerful approach to interpret dynamic cellular processes such as cell cycle and development. Still, however, accurate inference of trajectory is challenging. Recent development of RNA velocity provides an approach to visualize cell state transition without relying on prior knowledge. To perform TI and group cells based on RNA velocity we developed VeTra. By applying cosine similarity and merging weakly connected components, VeTra identifies cell groups from the direction of cell transition. Besides, VeTra suggests key regulators from the inferred trajectory. VeTra is a useful tool for TI and subsequent analysis.

Pancreatic β-cell dysfunction and reduced insulin secretion play a key role in the pathogenesis of diabetes. Fetal and neonatal islets are functionally immature and have blunted glucose responsiveness and decreased insulin secretion in response to stimuli and are far more proliferative. However, the mechanisms underlying functional immaturity are not well understood. Pancreatic islets are composed of a mixture of different cell types, and the microenvironment of islets and interactions between these cell types are critical for β-cell development and maturation. RNA sequencing and quantitative proteomic data from intact islets isolated from fetal (embryonic day 19) and 2-week-old Sprague-Dawley rats were integrated to compare their gene and protein expression profiles. Ingenuity Pathway Analysis (IPA) was also applied to elucidate pathways and upstream regulators modulating functional maturation of islets. By integrating transcriptome and proteomic data, 917 differentially expressed genes/proteins were identified with a false discovery rate of less than 0.05. A total of 411 and 506 of them were upregulated and downregulated in the 2-week-old islets, respectively. IPA revealed novel critical pathways associated with functional maturation of islets, such as AMPK (adenosine monophosphate-activated protein kinase) and aryl hydrocarbon receptor signaling, as well as the importance of lipid homeostasis/signaling and neuronal function. Furthermore, we also identified many proteins enriched either in fetal or 2-week-old islets related to extracellular matrix and cell communication, suggesting that these pathways play critical roles in islet maturation. Our present study identified novel pathways for mature islet function in addition to confirming previously reported mechanisms, and provided new mechanistic insights for future research on diabetes prevention and treatment.

Cancer-associated fibroblasts (CAFs) comprise a heterogeneous population of stromal cells within the tumour microenvironment. CAFs exhibit both tumour-promoting and tumour-suppressing functions, making them exciting targets for improving cancer treatments. A careful identification and characterisation of the CAF heterogeneity is thus necessary before implementing CAF-targeted strategies in cancer. With that in mind, we developed a flow cytometry strategy based on exclusion of non-CAF cells and successfully employed it to explore the temporal heterogeneity of CAFs in two models of triple-negative breast cancer (4T1 and 4T07). Analysing 128 murine tumours we identified 5-6 main CAF subpopulations and numerous minor ones based on the analysis of alpha smooth muscle actin, fibroblast activation protein alpha, platelet derived growth factor receptor alpha and beta, CD26/DPP4 and podoplanin. All markers showed temporal changes, and CD26+ CAFs emerged as a large novel subpopulation. These results form the foundation needed for the future elucidation of tumour-promoting CAF subpopulations.

Rationale: The heart undergoes dramatic developmental changes during the prenatal to postnatal transition, including maturation of cardiac myocyte energy metabolic and contractile machinery. Delineation of the mechanisms involved in cardiac postnatal development could provide new insight into the fetal shifts that occur in the diseased heart and unveil strategies for driving maturation of stem cell–derived cardiac myocytes. Objective: To delineate transcriptional drivers of cardiac maturation. Methods and Results: We hypothesized that ERR (estrogen-related receptor) α and γ, known transcriptional regulators of postnatal mitochondrial biogenesis and function, serve a role in the broader cardiac maturation program. We devised a strategy to knockdown the expression of ERRα and γ in heart after birth (pn-csERRα/γ [postnatal cardiac-specific ERRα/γ]) in mice. With high levels of knockdown, pn-csERRα/γ knockdown mice exhibited cardiomyopathy with an arrest in mitochondrial maturation. RNA sequence analysis of pn-csERRα/γ knockdown hearts at 5 weeks of age combined with chromatin immunoprecipitation with deep sequencing and functional characterization conducted in human induced pluripotent stem cell–derived cardiac myocytes (hiPSC-CM) demonstrated that ERRγ activates transcription of genes involved in virtually all aspects of postnatal developmental maturation, including mitochondrial energy transduction, contractile function, and ion transport. In addition, ERRγ was found to suppress genes involved in fibroblast activation in hearts of pn-csERRα/γ knockdown mice. Disruption of Esrra and Esrrg in mice during fetal development resulted in perinatal lethality associated with structural and genomic evidence of an arrest in cardiac maturation, including persistent expression of early developmental and noncardiac lineage gene markers including cardiac fibroblast signatures. Lastly, targeted deletion of ESRRA and ESRRG in hiPSC-CM derepressed expression of early (transcription factor 21 or TCF21) and mature (periostin, collagen type III) fibroblast gene signatures. Conclusions: ERRα and γ are critical regulators of cardiac myocyte maturation, serving as transcriptional activators of adult cardiac metabolic and structural genes, an.d suppressors of noncardiac lineages including fibroblast determination.

To process large-scale single-cell RNA-sequencing (scRNA-seq) data effectively without excessive distortion during dimension reduction, we present SHARP, an ensemble random projection-based algorithm that is scalable to clustering 10 million cells. Comprehensive benchmarking tests on 17 public scRNA-seq data sets show that SHARP outperforms existing methods in terms of speed and accuracy. Particularly, for large-size data sets (more than 40,000 cells), SHARP runs faster than other competitors while maintaining high clustering accuracy and robustness. To the best of our knowledge, SHARP is the only R-based tool that is scalable to clustering scRNA-seq data with 10 million cells.

Chromosome dynamics and cellular clocks Many genes undergo daily or circadian changes in their rate of transcription. Kim et al. explored the mechanism by which the circadian clock is linked to chromosome dynamics (see the Perspective by Diettrich Mallet de Lima and Göndör). They used a chromosome conformation capture technique (Hi-C) to identify the interactions of adjacent DNA fragments and determine how DNA looping that altered such interactions changed over daily cycles. The repressive transcription factor Rev-erbα, which functions as part of the mammalian clock mechanism, appears to bind to chromatin and recruit a protein complex that evicts other proteins that enhance looping, thus favoring enhancer-promoter interactions. Science , this issue p. 1274 ; see also p. 1212

Brown adipose tissue is a thermogenic organ that dissipates chemical energy as heat to protect animals against hypothermia and to counteract metabolic disease. However, the transcriptional mechanisms that determine the thermogenic capacity of brown adipose tissue before environmental cold are unknown. Here we show that histone deacetylase 3 (HDAC3) is required to activate brown adipose tissue enhancers to ensure thermogenic aptitude. Mice with brown adipose tissue-specific genetic ablation of HDAC3 become severely hypothermic and succumb to acute cold exposure. Uncoupling protein 1 (UCP1) is nearly absent in brown adipose tissue lacking HDAC3, and there is also marked downregulation of mitochondrial oxidative phosphorylation genes resulting in diminished mitochondrial respiration. Remarkably, although HDAC3 acts canonically as a transcriptional corepressor, it functions as a coactivator of oestrogen-related receptor α (ERRα) in brown adipose tissue. HDAC3 coactivation of ERRα is mediated by deacetylation of PGC-1α and is required for the transcription of Ucp1, Ppargc1a (encoding PGC-1α), and oxidative phosphorylation genes. Importantly, HDAC3 promotes the basal transcription of these genes independently of adrenergic stimulation. Thus, HDAC3 uniquely primes Ucp1 and the thermogenic transcriptional program to maintain a critical capacity for thermogenesis in brown adipose tissue that can be rapidly engaged upon exposure to dangerously cold temperature.